Bibliographic record
Abstract
Along with increasing local and global multiculturalism, many scholars are interested in understanding how multicultural children negotiate their identity and their ideological becoming in the sociocultural environment. This study (delves into) one 9-year-old Canadian-Korean boy’s understanding of his identity as a multicultural child while living in Seoul, South Korea. I used Bakhtin’s dialogic theory of language in this inquiry. I also used a narrative case study methodology to understand the diverse ways that he represents his sociocultural worlds by recounting his events or actions within and across cultures. I interviewed him in an in-depth way for three months. The main results were as follows. First, identity is a continuous, enacted, and negotiated process of becoming in different spaces and places. Second, one’s identity is strongly influenced by languages that one uses within his sociocultural contexts. Third, a child can be an active inquirer who voluntarily expresses, voices, and represents his diverse identities. These findings suggest that understanding a 9-year-old Canadian-Korean boy’s dynamic identity construction as a multicultural child is an important way to understand his diverse positionings, values, and beliefs in his social world. More qualitative inquiries of multicultural children and their voices are needed to better understand how multicultural children perceive, negotiate, and construct their identity in multiple sociocultural environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.044 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".